


import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__UpperCamelCase     		=							logging.get_logger(__name__)

__UpperCamelCase     		=							'▁'

__UpperCamelCase     		=							{'vocab_file': 'sentencepiece.bpe.model'}

__UpperCamelCase     		=							{
    'vocab_file': {
        'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large-finetuned-conll02-dutch': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll02-spanish': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-english': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-german': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'
        ),
    }
}

__UpperCamelCase     		=							{
    'xlm-roberta-base': 5_1_2,
    'xlm-roberta-large': 5_1_2,
    'xlm-roberta-large-finetuned-conll02-dutch': 5_1_2,
    'xlm-roberta-large-finetuned-conll02-spanish': 5_1_2,
    'xlm-roberta-large-finetuned-conll03-english': 5_1_2,
    'xlm-roberta-large-finetuned-conll03-german': 5_1_2,
}







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			_UpperCamelCase		:       Dict 			=		VOCAB_FILES_NAMES
			_UpperCamelCase		:       Dict 			=		PRETRAINED_VOCAB_FILES_MAP
			_UpperCamelCase		:       str 			=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_UpperCamelCase		:       int 			=		['input_ids', 'attention_mask']




			def __init__(				self   ,    snake_case   ,    snake_case="<s>"   ,    snake_case="</s>"   ,    snake_case="</s>"   ,    snake_case="<s>"   ,    snake_case="<unk>"   ,    snake_case="<pad>"   ,    snake_case="<mask>"   ,    snake_case = None   ,    **snake_case   ,    ):

						'''simple docstring'''


						UpperCamelCase__       =							AddedToken(snake_case   ,    lstrip=snake_case   ,    rstrip=snake_case  ) if isinstance(snake_case   ,    snake_case  ) else mask_token

						UpperCamelCase__       =							{} if sp_model_kwargs is None else sp_model_kwargs

						super().__init__(
						    bos_token=snake_case   ,    eos_token=snake_case   ,    unk_token=snake_case   ,    sep_token=snake_case   ,    cls_token=snake_case   ,    pad_token=snake_case   ,    mask_token=snake_case   ,    sp_model_kwargs=self.sp_model_kwargs   ,    **snake_case   ,    )

						UpperCamelCase__       =							spm.SentencePieceProcessor(**self.sp_model_kwargs  )
						self.sp_model.Load(str(snake_case  )  )
						UpperCamelCase__       =							vocab_file

						# Original fairseq vocab and spm vocab must be "aligned":
						# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
						# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
						# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
						# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

						# Mimic fairseq token-to-id alignment for the first 4 token
						UpperCamelCase__       =							{"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

						# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
						UpperCamelCase__       =							1

						UpperCamelCase__       =							len(self.sp_model  ) + self.fairseq_offset
						UpperCamelCase__       =							{v: k for k, v in self.fairseq_tokens_to_ids.items()}




			def __getstate__(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.__dict__.copy()
						UpperCamelCase__       =							None
						UpperCamelCase__       =							self.sp_model.serialized_model_proto()
						return state




			def __setstate__(				self   ,    snake_case  ):

						'''simple docstring'''


						UpperCamelCase__       =							d

						# for backward compatibility
						if not hasattr(self   ,    "sp_model_kwargs"  ):
									UpperCamelCase__       =							{}

						UpperCamelCase__       =							spm.SentencePieceProcessor(**self.sp_model_kwargs  )
						self.sp_model.LoadFromSerializedProto(self.sp_model_proto  )




			def 					snake_case__					(				self   ,    snake_case   ,    snake_case = None  ):

						'''simple docstring'''


						if token_ids_a is None:
									return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
						UpperCamelCase__       =							[self.cls_token_id]
						UpperCamelCase__       =							[self.sep_token_id]
						return cls + token_ids_a + sep + sep + token_ids_a + sep




			def 					snake_case__					(				self   ,    snake_case   ,    snake_case = None   ,    snake_case = False  ):

						'''simple docstring'''


						if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=snake_case   ,    token_ids_a=snake_case   ,    already_has_special_tokens=snake_case  )

						if token_ids_a is None:
									return [1] + ([0] * len(snake_case  )) + [1]
						return [1] + ([0] * len(snake_case  )) + [1, 1] + ([0] * len(snake_case  )) + [1]




			def 					snake_case__					(				self   ,    snake_case   ,    snake_case = None  ):

						'''simple docstring'''


						UpperCamelCase__       =							[self.sep_token_id]
						UpperCamelCase__       =							[self.cls_token_id]

						if token_ids_a is None:
									return len(cls + token_ids_a + sep  ) * [0]
						return len(cls + token_ids_a + sep + sep + token_ids_a + sep  ) * [0]




			@property
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						return len(self.sp_model  ) + self.fairseq_offset + 1  # Add the <mask> token




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							{self.convert_ids_to_tokens(snake_case  ): i for i in range(self.vocab_size  )}
						vocab.update(self.added_tokens_encoder  )
						return vocab




			def 					snake_case__					(				self   ,    snake_case  ):

						'''simple docstring'''


						return self.sp_model.encode(snake_case   ,    out_type=snake_case  )




			def 					snake_case__					(				self   ,    snake_case  ):

						'''simple docstring'''


						if token in self.fairseq_tokens_to_ids:
									return self.fairseq_tokens_to_ids[token]
						UpperCamelCase__       =							self.sp_model.PieceToId(snake_case  )

						# Need to return unknown token if the SP model returned 0
						return spm_id + self.fairseq_offset if spm_id else self.unk_token_id




			def 					snake_case__					(				self   ,    snake_case  ):

						'''simple docstring'''


						if index in self.fairseq_ids_to_tokens:
									return self.fairseq_ids_to_tokens[index]
						return self.sp_model.IdToPiece(index - self.fairseq_offset  )




			def 					snake_case__					(				self   ,    snake_case  ):

						'''simple docstring'''


						UpperCamelCase__       =							"".join(snake_case  ).replace(snake_case   ,    " "  ).strip()
						return out_string







			def 					snake_case__					(				self   ,    snake_case   ,    snake_case = None  ):

						'''simple docstring'''


						if not os.path.isdir(snake_case  ):
									logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''  )
									return
						UpperCamelCase__       =							os.path.join(
						    snake_case   ,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]  )

						if os.path.abspath(self.vocab_file  ) != os.path.abspath(snake_case  ) and os.path.isfile(self.vocab_file  ):
									copyfile(self.vocab_file   ,    snake_case  )
						elif not os.path.isfile(self.vocab_file  ):
									with open(snake_case   ,    "wb"  ) as fi:
												UpperCamelCase__       =							self.sp_model.serialized_model_proto()
												fi.write(snake_case  )

						return (out_vocab_file,)






import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
						import jax
						import jax.numpy as jnp
						from flax.jax_utils import replicate
						from flax.training.common_utils import shard







@slow
@require_flax
class 							lowerCamelCase__						(				unittest.TestCase   ):






			"""simple docstring"""






			def 					snake_case__					(				self  ):

						'''simple docstring'''


						super().tearDown()
						gc.collect()




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__,   UpperCamelCase__       =							FlaxControlNetModel.from_pretrained(
						    "lllyasviel/sd-controlnet-canny"   ,    from_pt=snake_case   ,    dtype=jnp.bfloataa  )
						UpperCamelCase__,   UpperCamelCase__       =							FlaxStableDiffusionControlNetPipeline.from_pretrained(
						    "runwayml/stable-diffusion-v1-5"   ,    controlnet=snake_case   ,    from_pt=snake_case   ,    dtype=jnp.bfloataa  )
						UpperCamelCase__       =							controlnet_params

						UpperCamelCase__       =							"bird"
						UpperCamelCase__       =							jax.device_count()
						UpperCamelCase__       =							pipe.prepare_text_inputs([prompts] * num_samples  )

						UpperCamelCase__       =							load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"  )
						UpperCamelCase__       =							pipe.prepare_image_inputs([canny_image] * num_samples  )

						UpperCamelCase__       =							jax.random.PRNGKey(0  )
						UpperCamelCase__       =							jax.random.split(snake_case   ,    jax.device_count()  )

						UpperCamelCase__       =							replicate(snake_case  )
						UpperCamelCase__       =							shard(snake_case  )
						UpperCamelCase__       =							shard(snake_case  )

						UpperCamelCase__       =							pipe(
						    prompt_ids=snake_case   ,    image=snake_case   ,    params=snake_case   ,    prng_seed=snake_case   ,    num_inference_steps=50   ,    jit=snake_case   ,    ).images
						assert images.shape == (jax.device_count(), 1, 768, 512, 3)

						UpperCamelCase__       =							images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]  )
						UpperCamelCase__       =							images[0, 253:256, 253:256, -1]

						UpperCamelCase__       =							jnp.asarray(jax.device_get(image_slice.flatten()  )  )
						UpperCamelCase__       =							jnp.array(
						    [0.167969, 0.116699, 0.081543, 0.154297, 0.132812, 0.108887, 0.169922, 0.169922, 0.205078]  )
						print(F'''output_slice: {output_slice}'''  )
						assert jnp.abs(output_slice - expected_slice  ).max() < 1E-2







			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__,   UpperCamelCase__       =							FlaxControlNetModel.from_pretrained(
						    "lllyasviel/sd-controlnet-openpose"   ,    from_pt=snake_case   ,    dtype=jnp.bfloataa  )
						UpperCamelCase__,   UpperCamelCase__       =							FlaxStableDiffusionControlNetPipeline.from_pretrained(
						    "runwayml/stable-diffusion-v1-5"   ,    controlnet=snake_case   ,    from_pt=snake_case   ,    dtype=jnp.bfloataa  )
						UpperCamelCase__       =							controlnet_params

						UpperCamelCase__       =							"Chef in the kitchen"
						UpperCamelCase__       =							jax.device_count()
						UpperCamelCase__       =							pipe.prepare_text_inputs([prompts] * num_samples  )

						UpperCamelCase__       =							load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"  )
						UpperCamelCase__       =							pipe.prepare_image_inputs([pose_image] * num_samples  )

						UpperCamelCase__       =							jax.random.PRNGKey(0  )
						UpperCamelCase__       =							jax.random.split(snake_case   ,    jax.device_count()  )

						UpperCamelCase__       =							replicate(snake_case  )
						UpperCamelCase__       =							shard(snake_case  )
						UpperCamelCase__       =							shard(snake_case  )

						UpperCamelCase__       =							pipe(
						    prompt_ids=snake_case   ,    image=snake_case   ,    params=snake_case   ,    prng_seed=snake_case   ,    num_inference_steps=50   ,    jit=snake_case   ,    ).images
						assert images.shape == (jax.device_count(), 1, 768, 512, 3)

						UpperCamelCase__       =							images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]  )
						UpperCamelCase__       =							images[0, 253:256, 253:256, -1]

						UpperCamelCase__       =							jnp.asarray(jax.device_get(image_slice.flatten()  )  )
						UpperCamelCase__       =							jnp.array(
						    [[0.271484, 0.261719, 0.275391, 0.277344, 0.279297, 0.291016, 0.294922, 0.302734, 0.302734]]  )
						print(F'''output_slice: {output_slice}'''  )
						assert jnp.abs(output_slice - expected_slice  ).max() < 1E-2


